Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia

To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt” (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 \u3e 0.8) for one-month prediction and a relatively lower value (R2 = 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin

THE ROLE OF RISK ASSESSMENT IN MANAGING RISK

Risk and Uncertainty,

The Grand Ethiopian Renaissance Dam: Source of Cooperation or Contention?

This paper discusses the challenges and benefits of the Grand Ethiopian Renaissance Dam (GERD), which is under construction and expected to be operational on the Blue Nile River in Ethiopia in a few years. Like many large-scale projects on transboundary rivers, the GERD has been criticized for potentially jeopardizing downstream water security and livelihoods through upstream unilateral decision making. In spite of the contentious nature of the project, the authors argue that this project can provide substantial benefits for regional development. The GERD, like any major river infrastructure project, will undeniably bring about social, environmental, and economic change, and in this unique case has, on balance, the potential to achieve success on all fronts. It must be stressed, however, that strong partnerships between riparian countries are essential. National success is contingent on regional cooperation

Small strain stiffness within logarithmic contractancy model for structured anisotropic clay.

ABSTRACT: Stiffness of soils in the small strain region is high and it decays nonlinearly with increasing shear strains or with mobilization of shear stresses. However, the commonly used critical state based constitutive models use a simple elastic formulation at small strains that falls short in the prediction of the small strain nonlinearity and anisotropy. This paper proposes a simple way for rendering the existing constitutive models with the capability to capture the small strain behaviour of soils. This is illustrated by proposing a new model for structured anisotropic clay extending an existing model that uses the framework of logarithmic contractancy called ESCLAY1S. The proposed model is implemented into a Finite Element program as a user-defined soil model. The model predictions are compared with experimental data for various clays. Furthermore, the effect of nonlinearity is investigated for an excavation in soft clay

Modern contraceptive use and associated factors during extended postpartum period among women who gave birth in the last 12 months at Northwest Ethiopia

Background: The extended postpartum period is a one-year follow-up period after giving birth, and it is critical for women to prevent unintended pregnancy and reduce the risk of maternal and child mortality by ensuring safe birth intervals. Many women, however, are unaware that they are at risk for pregnancy throughout this period. Hence, the aim of this study was to assess the utilization and associated factors of modern contraceptives during extended postpartum family planning (EPPP) in northwest Ethiopia. Methods: A community-based cross-sectional study design was conducted using 630 samples from October 01 to October 30, 2020, in northwest Ethiopia. The study participants were drawn through a multistage sampling technique and data were collected using structured questionnaires via interview. The collected data were entered into EpiData version 4.2 and exported into SPSS version 25.0 for management and further analysis. A bivariable logistic regression model was used to identify variables having an association with the outcome variable. In bivariable analysis, variables having P ≤ 0.25 were selected and entered into multivariable logistic regression analysis. Finally, in multivariable analysis, variables having P ≤ 0.05 with a 95% CI were declared as significantly associated with the outcome variable. Results: About 60.6% of women were using modern contraceptive during extended postpartum period. Mothers to partner discussion (AOR= 7.6, 95% CI: 4.20– 14.05), secondary educational status (AOR= 3.8, 95% CI: 1.36– 10.93), college and above educational status (AOR= 7, 95% CI: 1.92– 25.57), menstrual resumption (AOR= 9.2, 95% CI: 5.66– 15.12), sex resumed (AOR=8.5, 95% CI: 2.19– 33.58), fertility desire (AOR= 3.9, 95% CI: 1.99– 6.15), linkage to FP during child immunization (AOR= 2.7, 95% CI: 1.67– 4.50), and FP counseling during pregnancy (AOR=2, 95% CI: 1.25– 3.34) were significantly associated with outcome variable. Conclusion: Associating factors were identified as partner discussion, education, menstrual resumption, fertility desire, sexual resumption, FP counseling, and FP during child immunization. Improving mothers’ education and informing couples about the dangers of becoming pregnant before menstruation are critical

Interaction-free measurement and forward scattering

Interaction-free measurement is shown to arise from the forward-scattered wave accompanying absorption: a "quantum silhouette" of the absorber. Accordingly, the process is not free of interaction. For a perfect absorber the forward-scattered wave is locked both in amplitude and in phase. For an imperfect one it has a nontrivial phase of dynamical origin (``colored silhouette"), measurable by interferometry. Other examples of quantum silhouettes, all controlled by unitarity, are briefly discussed.Comment: 4 pages in RevTex + 1 figure in eps; submitted to Phys. Rev. A since 09Jan98; now update

REAL-TIME SENSOR DATA ANALYTICS AND VISUALIZATION IN CLOUD-BASED SYSTEMS FOR FOREST ENVIRONMENT MONITORING

Forest environment monitoring is essential for natural resource management. The development of sensors using across forests enables for the collection massive volumes of data due to technological improvements in the sensor network. Raspberry Pi, a flexible and inexpensive single-board computer, is at the main of the system, connecting and interfacing with the many sensors spread throughout the system. Sensors such as this can collect crucial information about the forest's environment, such as the weather, humidity, and temperature. Data from various sensors can be acquired and processed in real-time due to Raspberry Pi's role as a data collection device. The system uses cloud-based services to overcome the limitations of on-premises data processing and storage. A fusion technique on the cloud platform combines and analyzes data from various sensors after receiving transmissions from Raspberry Pi. The cloud service provides a location for live monitoring and other visualization which greatly help data in real-time. These visuals can be accessed remotely, allowing users to access the forest from any location. Improved comprehension and control of forest environments are possible because of the combination of various technologies for collecting, analyzing, and evaluating sensor data

High-efficiency quantum interrogation measurements via the quantum Zeno effect

The phenomenon of quantum interrogation allows one to optically detect the presence of an absorbing object, without the measuring light interacting with it. In an application of the quantum Zeno effect, the object inhibits the otherwise coherent evolution of the light, such that the probability that an interrogating photon is absorbed can in principle be arbitrarily small. We have implemented this technique, demonstrating efficiencies exceeding the 50% theoretical-maximum of the original ``interaction-free'' measurement proposal. We have also predicted and experimentally verified a previously unsuspected dependence on loss; efficiencies of up to 73% were observed and the feasibility of efficiencies up to 85% was demonstrated.Comment: 4 pages, 3 postscript figures. To appear in Phys. Rev. Lett; submitted June 11, 199